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Bigeye tuna (Thunnus obesus) is one of the commercially important pelagic species that caught mostly in the eastern Indian Ocean. This species prefers to stay close, and is usually below the thermocline layer. Remotely sensed data was used to determine the characteristics of Bigeye tuna fishing areas at a depth of 155 meter. Fishing vessels for Bigeye tuna were obtained from vessel monitoring systems (VMS) from January through December, 2015-2016. Daily data on sub-surface temperature (SST), sub-surface chlorophyll-a concentration (SSC), and sub-surface salinity (SSS) were obtained from the INDESO Project website. All oceanographic parameter data were selected at a depth of 155 m. The position of Bigeye tuna and oceanographic data were then grouped into 2 group monsoon, southeast monsoon (April – September) and northwest monsoon (October – March). The results showed that, during the southeast and northwest monsoon, Bigeye tuna mostly found in SSC of 0.03 – 0.05 mg/m3, SST of 16° - 18°C and salinity of 34 psu. These results showed that at depth of 155 m, Bigeye Tuna prefers to stay in small chl-a (0.03 – 0.04 mg/m3), low SST (16° - 18°C) and salinity of 34 psu. These information were essential and could be used to support fisheries management decisions especially for Bigeye Tuna in the eastern Indian Ocean.

Remotely sensed data and habitat model approach were employed to evaluate the present of oceanographic aspect in the Bigeye tuna's potential fishing zone (PFZ) at a profundity of 155 m. Vessel monitoring system was employed to acquire the angling vessels for Bigeye tuna from January through December, 2015-2016. Daily data of sub-surface temperature (Sub_ST), sub-surface chlorophyll-a (Sub_SC), and sub-surface salinity (Sub_SS) were downloaded from INDESO Project website. Vessel monitoring system and environmental data were employed for maximum entropy (maxent) model development. The model predictive achievement was then estimated applying the area under the curve (AUC) value. Maxent model results (AUC>0.745) exhibited its probable to understand the Bigeye tuna's spatial dispersion on the specific sub-surface. In addition, the results also showed Sub_ST (43,1%) was the most affective aspect in the Bigeye tuna dispersion, pursued by Sub_SC (35,2%) and Sub_SS (21,6%).

Perairan selatan Aru merupakan daerah penangkapan ikan yang potensial dengan sumberdaya ikannya yang berlimpah. Kapal-kapal ikan melakukan penangkapan ikan dengan mempertimbangkan kondisi kesuburan perairan yaitu fenomena upwelling melalui parameter suhu permukaan laut dan kelimpahan konsentrasi klorofil-a. Penggunaan citra MODIS secara periodik dapat menggambarkan waktu dan lokasi upwelling. Variabilitas suhu permukaan laut dan konsentrasi klorofil-a di lintang 7 LS dan 7.5 LS, bujur 133.1 – 136 BT menunjukkan fenomena upwelling pada musim Timur dimana suhu permukaan laut menurun hingga 24 oC sedangkan konsentrasi klorofil-a rata-rata meningkat hingga 3.61 mg/m3. Fenomena downwelling terjadi di musim Barat dimana suhu permukaan laut di lintang yang sama menjadi hangat berkisar 30 oC dengan konsentrasi rata-rata klorofil-a 3. Jumlah kapal ikan yang beroperasi di daerah penangkapan diketahui dengan menggunakan citra Radarsat-2 dan data VMS yang disesuaikan dengan waktu pengambilan citra di setiap bulannya dengan lokasi yang sama. Analisis spasial dan temporal dilakukan untuk mengetahui sebaran kapal ikan pada musim timur saat kesuburan perairan tinggi dimana konsentrasi klorofil-a di titik penangkapan mencapai 5.01 mg/m3 dan jumlah kapal ikan yang beroperasi >150 unit/hari. Pada musim Barat kesuburan perairan rendah dimana konsentrasi klorofil-a di titik penangkapan 3 dan jumlah kapal ikan yang beroperasi KATA KUNCIklorofil-a, Modis, perairan selatan Aru, Radarsat-2, suhu permukaan laut. SPATIAL AND TEMPORAL ANALYSIS OF WATER PRODUCTIVITY AFFECTING ACTIVITIES IN FISHING VESSEL AT SOUTH ARU FISHING GROUND USING MODIS IMAGE AND RADARSAT-2Southern Aru waters is a potential fishing areas with abundant fish resources. Fishing boats catch fish by considering the condition of the waters productivity such as upwelling phenomenon through the parameters of sea surface temperatures and an abundance of chlorophyll-a concentration. The use of MODIS imagery can periodically describe the timing and location of upwelling. Variability of sea surface temperature and chlorophyll-a concentration in latitude 7 LS and 7.5 LS, longitude 133.1 - 136 BT showed the phenomenon of upwelling in the east season where sea surface temperature decreased from 30° C to 24° C while the chlorophyll-a concentration increased by an average of 0,3 mg/m3 to 3.61 mg/m3. Downwelling phenomenon took place in the west season where the sea surface temperature in the same latitude range 30° C to be warm with an average concentration of chlorophyll-a 3. The number of fishing vessels operating in the fishing area known using RADARSAT-2 imagery and data VMS were later adapted to capture images of each month at the same location. Based on the spatial and temporal analysis was conducted to determine the distribution of fishing vessels in the eastern summer time high waters productivity where the concentration of chlorophyll-a at the point reached 5:01 mg m3, the number of fishing vessels in operation increased to 150 units/ ay. While in the West season low waters productivity where the concentration of chlorophyll-a at the point of 3 and the number of ships in operation decreased to below 40 units/day. This suggests that the increase in the number of fishing fleets affected by the level of waters productivity or fishing season in the East to Transition II at the southern Aru Islands waters. Keywords: Chlorophyll-a, Modis, the south of Aru, RADARSAT-2, Sea surface temperature.

Bigeye tuna (Thunnus obesus) is one of the commercially important fish in the eastern Indian Ocean and is a highly migratory species. A modeling approach and remotely sensed data were used to develop an appropriate prediction model and to understand the contribution of oceanographic factors in the distribution of Bigeye tuna in 2 different depths. The daily data of sub-surface chlorophyll-a (SSC), sub-surface temperature (SST) and sub-surface salinity (SSS) were downloaded from infrastructure development of space oceanography (INDESO) project website, meanwhile fishing vessel for Bigeye tuna were obtained from vessel monitoring system (VMS) from April through September 2015 - 2016. The daily VMS data and environmental factors were used for maximum entropy model construction. The predictive model performance was then assessed using a threshold-independent metric, the area under the curve (AUC) metric of the receiver operating characteristic (ROC). Maximum entropy model results based on AUC (more than 0.80) indicated its potential to deduce the spatial distribution of Bigeye tuna. At a depth of 155 m, SSC (37.9%) is the most effective variable in the Bigeye tuna distribution, followed by SST (32.7 %) and SSS (29.4%), meanwhile at a depth of 222 m, SSS (46.8%) is the most important variable followed by SST (31.3%) and SSC (21.9%). Combination of a modelling approach and multi-sensor remote sensing data offers an innovative way to determine the potential fishing zone of the Bigeye tuna in the eastern Indian Ocean.